Revision as of 21:01, 13 June 2017

General introduction to the project and the main parties involved in it.

Example introduction: Project Name: Project Title is a NSF/NIH/etc funded project involving a collaboration between Kno.e.sis Center, Wright State University and Party-1, Party-2.

Background

Wisdom of crowds (WoC) refers to a form of collective intelligence in which the aggregate judgment of a group is reliably superior to that of any one of its individual members [1]. WoC effects have been proposed to explain, for example, the underperformance of individually managed stock funds compared to passive index funds, the superiority of prediction markets versus political pundits in predicting election outcomes, and even the relative success of democracies over other forms of government [2].

For a crowd to be wise, however, it is essential that its members possess diverse information. The explanation linking diversity to collective intelligence is that analysts who possess diverse information are more likely to produce uncorrelated errors that cancel one another out, thereby yielding a collective judgment close to the truth [3]. Although one way of maximizing diversity is to have analysts work independently, this is often impractical given the increasingly collaborative nature of today’s analytic work environments [4]. But even if independence could be enforced, doing so would risk forfeiting the well-documented benefits of collaboration [5]. What is needed therefore are methods for identifying subgroups (or subnetworks) of diverse individuals within larger collaborative networks.

This project aims to develop socio-cognitive framework for understanding and modeling diversity in human social networks. We emphasize the social aspect of diversity because differences in individuals’ knowledge and viewpoints are often shaped as much by differences in their peer networks as by their individual experiences and thought processes [6] – such that it is possible to infer a person’s viewpoints in part by the company he or she keeps. Similarly, we stress the cognitive dimension of diversity because what a person knows, and how a person thinks, are measurable cognitive constructs that directly influence diversity of judgments across individuals.

Research Objectives

At a theoretical level, we seek to understand what are the appropriate dimensions (or features) for characterizing social-cognitive diversity in human networks. At an empirical level, we want to know which approaches for computing diversity produce metrics that are most predictive of collective intelligence. More specifically, we aim to answer following questions,

(1) Do current popular social media platforms such as Twitter contain sufficient data for measuring socio-cognitive diversity?

(2) Can socio-cognitive diversity be computed automatically using natural language processing (NLP) techniques and social network analysis?

People (ordered by last name)

PI

Affiliation-1: Name-1 (Linked to webpage), Name-2 (Linked to webpage), ...

Affiliation-.: Name-...

Co-PI

Affiliation-1: Name-1 (Linked to webpage), Name-2 (Linked to webpage), ...

Affiliation-.: Name-...

Researchers

Affiliation-1: Name-1 (Linked to webpage), Name-2 (Linked to webpage), ...

Affiliation-.: Name-...

Publications

1. [Link_to_Paper Paper_Title]

1. [Link_to_Paper Paper_Title]

News

• [Link_to_news News_title]

• [Link_to_news News_title]

Funding

This project is sponsored by NSF/NIH/etc Grant No. (NUMBER) to the Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis) <if any> /and The-Other=Party/

<Logo of the sponsor>

Related Projects

• Full List of Kno.e.sis Research Projects

Concurrent Projects

• [Link_to_project Project_Title]

• [Link_to_project Project_Title]

Prior Projects

• [Link_to_project Project_Title]

• [Link_to_project Project_Title]